# -*- coding: utf-8 -*-
"""lung cancerdetection.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1f7VybSnYLPbUVLRLMNQboxQkCYaBCXMs
"""


# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os


# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

# importing libraries

import tensorflow as tf
from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow.keras.models import Sequential
import numpy as np
from glob import glob
import matplotlib.pyplot as plt

image_set =  "./lung_image_sets"

SIZE_X = SIZE_Y = 224

datagen = tf.keras.preprocessing.image.ImageDataGenerator(validation_split = 0.2)

train_set = datagen.flow_from_directory(image_set,
                                       class_mode = "categorical",
                                       target_size = (SIZE_X,SIZE_Y),
                                       color_mode="rgb",
                                       batch_size = 128, 
                                       shuffle = False,
                                       subset='training',
                                       seed = 42)

validate_set = datagen.flow_from_directory(image_set,
                                       class_mode = "categorical",
                                       target_size = (SIZE_X, SIZE_Y),
                                       color_mode="rgb",
                                       batch_size = 128, 
                                       shuffle = False,
                                       subset='validation',
                                       seed = 42)


IMAGE_SIZE = [224, 224]

resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)

# don't train existing weights
for layer in resnet.layers:
    layer.trainable = False

flatten = Flatten()(resnet.output)
dense = Dense(256, activation = 'relu')(flatten)
dense = Dense(128, activation = 'relu')(dense)
prediction = Dense(3, activation = 'softmax')(dense)

#creating a model
model = Model(inputs = resnet.input, outputs = prediction )

model.summary()

model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

#executing the model
history = model.fit(train_set, validation_data = (validate_set), epochs = 8, verbose = 1)

# plotting the loss
plt.plot(history.history['loss'],label = 'train_loss')
plt.plot(history.history['val_loss'], label = 'testing_loss')
plt.title('loss')
plt.legend()
plt.show()

# Both Validation and Training accuracy is shown here

plt.plot(history.history['accuracy'], label='training_accuracy')
plt.plot(history.history['val_accuracy'], label='validation accuracy')
plt.title('Accuracy')
plt.legend()
plt.show()

# CHECKING THE CONFUSION MATRIX

from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
Y_pred = model.predict(validate_set)
y_pred = np.argmax(Y_pred ,axis =1)
print('Confusion Matrix')
confusion_matrix = confusion_matrix(validate_set.classes, y_pred)
print(confusion_matrix)
print('Classification Report')
target_names = ['aca','n', 'scc']
print(classification_report(validate_set.classes, y_pred, target_names=target_names))

result = model.evaluate(validate_set,batch_size=128)
print("test_loss, test accuracy",result)

import pickle

with open('model_pkl', 'wb') as files:
    pickle.dump(model, files)

# img = tf.keras.utils.load_img('/content/lung_colon_image_set/lung_image_sets/lung_aca/lungaca1.jpeg', target_size=(224, 224))
# img_array = tf.keras.utils.img_to_array(img)
# img_array = tf.expand_dims(img_array, 0)

# # load saved model
# with open('model_pkl' , 'rb') as f:
#     lr = pickle.load(f)
#     predi=lr.predict(img_array)
#     print(predi)
#     image_output_class=target_names[np.argmax(predi)]

# print("The predicted class is", image_output_class)

import gradio as gd
from PIL import Image

css_class="""
body{ background-color:rgb(10, 30, 75)}
ul>li{
    text-decoration: none;
    list-style:none;
    margin: 1px;
    padding:.5px
}
h3{
    color: rgb(24, 46, 98);
    margin: 1px;
    padding:.5px
    text-align: center;
}
h4{
    text-decoration: underline;
    color: rgb(218, 57, 57);
    text-align: center;
}

"""
def acaClassOutput():
    return '''
    <h3>You CT Scan Report:-</h3>
    <hr>
    <h4>You have Adenocarcinoma type cancer</h4>
    <p>It is Non-small cell type cancer which has effected you 40% of lung cells.</p>
    <ul>
    <h4>You can try These cautions</h4>
    <li>Try Radiation therapy, Chemotherapy, Targeted therapy, Immunotherapy</li>
    <li>Try to stay away from Smokers and air pollution</li>
    <li>Concern with your doctor for more details.</li>
    </ul>
    '''

def sccClassOutput():
    return '''
    <h3>You CT Scan Report:-</h3>
    <hr>
    <h4>You have Squamous type cancer</h4>
    <p>It effects the broncial tube of lungs. You probably have smoke history as it effected your 30% lungs</p>
    <ul>
    <h4>You can try These cautions</h4>
    <li>Try Radiation therapy, Chemotherapy, Targeted therapy, Immunotherapy</li>
    <li>Try to stay away from Smokers and air pollution</li>
    <li>Concern with your doctor for more details.</li>
    '''

def nClassOutput():
    return '''
    <h3>You CT Scan Report:-</h3>
    <hr>
    <h4>You have Neuroendocrine type cancer</h4>
    <p>This type of cancer effect neuroendocrine which are responsible for producing harmones. This is less common than other types</p>
    <ul>
    <h4>You can try These cautions</h4>
    <li>Try regular screening if you have smoke history. Try surgeries</li>
    <li>Try to stay away from Smokers and air pollution</li>
    <li>Concern with your doctor for more details.</li>
    '''
# target_names = ['aca','n', 'scc']
def predictOutPut(image_class):
    output=''
    if(image_class=='aca'):
        output=acaClassOutput()
    elif(image_class=='n'):
        output=nClassOutput()
    elif(image_class=='scc'):
        output=sccClassOutput()
    return output

def greet_user(CTScanImage):
    image=gd.inputs.Image()
    pil_image = Image.fromarray(CTScanImage.astype('uint8'), 'RGB')
    pil_image_resized = pil_image.resize((224,224))
    img_array = tf.keras.utils.img_to_array(pil_image_resized)
    img_array = tf.expand_dims(img_array, 0)
    with open('model_pkl' , 'rb') as f:
        lr = pickle.load(f)
        predi=lr.predict(img_array)
        image_output_class=target_names[np.argmax(predi)]
    return  predictOutPut(image_output_class)

customInput=gd.inputs.Image(label="Upload You CT Scanned Image")
customOutput=gd.outputs.HTML(label="Your CT scan Report")
app =  gd.Interface(fn = greet_user, inputs=customInput, outputs=customOutput,title="Lung Cancer Detection", description="Upload your CT Scan Image to know Whether You have cancer or not",css=css_class)
app.launch()